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When Innovation Leaves People Behind: Reframing Accountability in Commercial Digital Health.

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Title: When Innovation Leaves People Behind: Reframing Accountability in Commercial Digital Health.
Authors: Dambha-Miller H; Primary Care Research Centre, University of Southampton, University Road, Southampton, England, SO17 1BJ, United Kingdom, 44 (0)23 8059 ext 5000., Smith L; Primary Care Research Centre, University of Southampton, University Road, Southampton, England, SO17 1BJ, United Kingdom, 44 (0)23 8059 ext 5000., Michels LV; Primary Care Research Centre, University of Southampton, University Road, Southampton, England, SO17 1BJ, United Kingdom, 44 (0)23 8059 ext 5000.
Source: Journal of medical Internet research [J Med Internet Res] 2026 Sep 17; Vol. 28, pp. e100654. Date of Electronic Publication: 2026 Sep 17.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
Imprint Name(s): Publication: <2011- > : Toronto : JMIR Publications
Original Publication: [Pittsburgh, PA? : s.n., 1999-
MeSH Terms: Digital Health* , Social Responsibility* , Digital Technology*, Humans
Abstract: Unlabelled: Digital technologies are increasingly integrated across health care systems; however, accumulating evidence shows that these tools often perform unevenly across population groups. Biases in AI-enabled tools can reinforce existing health inequities, particularly when systems are developed and validated using datasets that exclude underserved populations. Studies demonstrate systematic underestimation of illness severity, diagnostic inaccuracies, and measurement bias affecting ethnic minority groups, rural populations, people with disabilities, people experiencing homelessness, and other groups with limited access to health care. These disparities highlight structural vulnerabilities in the data that inform machine learning systems, where socially patterned access to care shapes what is recorded and therefore learned. Commercial adoption patterns further exacerbate inequities due to socioeconomic gradients in digital engagement and a lack of accountability measures for adherence to regulatory frameworks within National Health Service procurement processes. In this Viewpoint, we argue that ensuring accountability in digital health care requires transparent reporting of subgroup performance, representative development of datasets, and ongoing monitoring of distributional impacts. Achieving equitable and reliable digital health care demands regulatory and methodological standards that prioritize fairness and generalizability within the commercialization sector and are monitored across the pre- and postdeployment life cycle.
(© Hajira Dambha-Miller, Lucy Smith, Lysanne Veerle Michels. Originally published in the Journal of Medical Internet Research (https://www.jmir.org).)
References: PLOS Digit Health. 2025 May 20;4(5):e0000864. (PMID: 40392801)
JMIR Ment Health. 2026 Aug 25;13:e99143. (PMID: 42641123)
JAMA Intern Med. 2018 Nov 1;178(11):1544-1547. (PMID: 30128552)
JAMA Netw Open. 2023 Dec 1;6(12):e2345050. (PMID: 38100101)
Front Public Health. 2023 Sep 26;11:1196596. (PMID: 37822534)
Ann Intern Med. 2018 Dec 18;169(12):866-872. (PMID: 30508424)
NPJ Digit Med. 2023 Sep 14;6(1):172. (PMID: 37709945)
Health Policy. 2024 Aug;146:105122. (PMID: 38986333)
JAMA Dermatol. 2018 Nov 1;154(11):1247-1248. (PMID: 30073260)
J Med Internet Res. 2022 Mar 21;24(3):e34144. (PMID: 35311682)
Science. 2019 Oct 25;366(6464):447-453. (PMID: 31649194)
N Engl J Med. 2020 Dec 17;383(25):2477-2478. (PMID: 33326721)
Nat Med. 2021 Dec;27(12):2176-2182. (PMID: 34893776)
Patterns (N Y). 2021 Oct 08;2(10):100347. (PMID: 34693373)
J Biomed Inform. 2021 Jan;113:103621. (PMID: 33220494)
Contributed Indexing: Keywords: bias; digital health; health care; inequity; public health
Entry Date(s): Date Created: 20260917 Date Completed: 20260917 Latest Revision: 20260922
Update Code: 20260922
PubMed Central ID: PMC13585304
DOI: 10.2196/100654
PMID: 42753250
Database: MEDLINE
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  Data: Unlabelled: Digital technologies are increasingly integrated across health care systems; however, accumulating evidence shows that these tools often perform unevenly across population groups. Biases in AI-enabled tools can reinforce existing health inequities, particularly when systems are developed and validated using datasets that exclude underserved populations. Studies demonstrate systematic underestimation of illness severity, diagnostic inaccuracies, and measurement bias affecting ethnic minority groups, rural populations, people with disabilities, people experiencing homelessness, and other groups with limited access to health care. These disparities highlight structural vulnerabilities in the data that inform machine learning systems, where socially patterned access to care shapes what is recorded and therefore learned. Commercial adoption patterns further exacerbate inequities due to socioeconomic gradients in digital engagement and a lack of accountability measures for adherence to regulatory frameworks within National Health Service procurement processes. In this Viewpoint, we argue that ensuring accountability in digital health care requires transparent reporting of subgroup performance, representative development of datasets, and ongoing monitoring of distributional impacts. Achieving equitable and reliable digital health care demands regulatory and methodological standards that prioritize fairness and generalizability within the commercialization sector and are monitored across the pre- and postdeployment life cycle.&lt;br /&gt; (&#169; Hajira Dambha-Miller, Lucy Smith, Lysanne Veerle Michels. Originally published in the Journal of Medical Internet Research (https://www.jmir.org).)
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  Data: PLOS Digit Health. 2025 May 20;4(5):e0000864. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2240392801%22&quot;&gt;40392801)&lt;/searchLink&gt;&lt;br /&gt;JMIR Ment Health. 2026 Aug 25;13:e99143. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2242641123%22&quot;&gt;42641123)&lt;/searchLink&gt;&lt;br /&gt;JAMA Intern Med. 2018 Nov 1;178(11):1544-1547. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2230128552%22&quot;&gt;30128552)&lt;/searchLink&gt;&lt;br /&gt;JAMA Netw Open. 2023 Dec 1;6(12):e2345050. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2238100101%22&quot;&gt;38100101)&lt;/searchLink&gt;&lt;br /&gt;Front Public Health. 2023 Sep 26;11:1196596. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2237822534%22&quot;&gt;37822534)&lt;/searchLink&gt;&lt;br /&gt;Ann Intern Med. 2018 Dec 18;169(12):866-872. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2230508424%22&quot;&gt;30508424)&lt;/searchLink&gt;&lt;br /&gt;NPJ Digit Med. 2023 Sep 14;6(1):172. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2237709945%22&quot;&gt;37709945)&lt;/searchLink&gt;&lt;br /&gt;Health Policy. 2024 Aug;146:105122. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2238986333%22&quot;&gt;38986333)&lt;/searchLink&gt;&lt;br /&gt;JAMA Dermatol. 2018 Nov 1;154(11):1247-1248. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2230073260%22&quot;&gt;30073260)&lt;/searchLink&gt;&lt;br /&gt;J Med Internet Res. 2022 Mar 21;24(3):e34144. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2235311682%22&quot;&gt;35311682)&lt;/searchLink&gt;&lt;br /&gt;Science. 2019 Oct 25;366(6464):447-453. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2231649194%22&quot;&gt;31649194)&lt;/searchLink&gt;&lt;br /&gt;N Engl J Med. 2020 Dec 17;383(25):2477-2478. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2233326721%22&quot;&gt;33326721)&lt;/searchLink&gt;&lt;br /&gt;Nat Med. 2021 Dec;27(12):2176-2182. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2234893776%22&quot;&gt;34893776)&lt;/searchLink&gt;&lt;br /&gt;Patterns (N Y). 2021 Oct 08;2(10):100347. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2234693373%22&quot;&gt;34693373)&lt;/searchLink&gt;&lt;br /&gt;J Biomed Inform. 2021 Jan;113:103621. (PMID: &lt;searchLink fieldCode=&quot;PM&quot; term=&quot;%2233220494%22&quot;&gt;33220494)&lt;/searchLink&gt;
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